{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIDA - Automatic Generation of Visualizations and Infographics using Large Language Models\n",
    "\n",
    "LIDA is a library for generating data visualizations and data-faithful infographics. LIDA is grammar agnostic (will work with any programming language and visualization libraries e.g. matplotlib, seaborn, altair, d3 etc) and works with multiple large language model providers (OpenAI, PaLM, Cohere, Huggingface). Details on the components of LIDA are described in the [paper here](https://arxiv.org/abs/2303.02927) and in this tutorial [notebook](notebooks/tutorial.ipynb). See the project page [here](https://microsoft.github.io/lida/) for updates!.\n",
    "\n",
    "\n",
    "\n",
    "## Getting Started | Installation\n",
    "\n",
    "```bash \n",
    "pip install -U lida\n",
    "```\n",
    "\n",
    "If you intend to use lida with local huggingface models, you will need to install the `transformers` library. \n",
    "\n",
    "```bash\n",
    "pip install lida[transformers]\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The LIDA Python API\n",
    "\n",
    "Lida offers a manager class that exposes core functionality of the LIDA system. This tutorial will show you how to use the manager class to create visualizations based on a dataset.\n",
    "\n",
    "### Multiple LLM Backends\n",
    "LIDA supports multiple LLM backends such as `openai`, `cohere`, `palm`, `huggingface` etc. You can switch between backends by setting the `text_gen` parameter in the `Manager` class. By default, LIDA uses the `openai` backend. For a list of supported models and how to configure them, see the [llmx documentation](https://github.com/victordibia/llmx).\n",
    "\n",
    "```python\n",
    "\n",
    "from lida import llm\n",
    "\n",
    "text_gen = llm(\"openai\") # for openai\n",
    "text_gen = llm(provider=\"openai\", api_type=\"azure\", azure_endpoint=os.environ[\"AZURE_OPENAI_BASE\"], api_key=os.environ[\"AZURE_OPENAI_API_KEY\"],    api_version=\"2023-07-01-preview\") # for azure openai\n",
    "text_gen = llm(\"cohere\") # for cohere\n",
    "text_gen = llm(\"palm\") # for palm\n",
    "text_gen = llm(provider=\"hf\", model=\"uukuguy/speechless-llama2-hermes-orca-platypus-13b\", device_map=\"auto\")\n",
    "\n",
    "lida = Manager(text_gen=text_gen)\n",
    "```\n",
    "\n",
    "Note that you can set your llm keys as follows\n",
    "\n",
    "```bash\n",
    "export OPENAI_API_KEY=<your key>\n",
    "export COHERE_API_KEY=<your key>\n",
    "# for PaLM\n",
    "export PALM_SERVICE_ACCOUNT_KEY_FILE=<path to gcp service account key file>\n",
    "export PALM_PROJECT_ID=<your gcp project id>\n",
    "```\n",
    "#### Azure OpenAI\n",
    "```python\n",
    "from llmx import  llm, TextGenerationConfig\n",
    "import os \n",
    "\n",
    "text_gen = llm(\n",
    "    provider=\"openai\",\n",
    "    api_type=\"azure\",\n",
    "    azure_endpoint=os.environ[\"AZURE_OPENAI_BASE\"],\n",
    "    api_key=os.environ[\"AZURE_OPENAI_API_KEY\"],\n",
    "    api_version=\"2023-07-01-preview\",\n",
    ")\n",
    "lida = Manager(text_gen=text_gen)\n",
    "```\n",
    "\n",
    "\n",
    "### Summarization Methods \n",
    "The summarizer module works takes an `summary_method` argument which determines if the base summary is enriched by an LLM. By default, the `summary_method` argument is set to `default` for a base summary (statistics etc). Set it to `llm` to enrich/annotate the base summary with an llm.\n",
    "\n",
    "### Caching \n",
    "Each manager method takes a [`textgen_config`](https://github.com/victordibia/llmx/blob/7c0fc093d1b8780ebebc7e080f5c63991514038b/llmx/datamodel.py#L22C10-L22C10) argument which is a dictionary that can be used to configure the text generation process (with parameters for model, temperature, max_tokens, topk etc). One of the keys in this dictionary is `use_cache`. If set to `True`, the manager will cache the generated text associated with that method. Use for speedup and to avoid hitting API limits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install -U lida \n",
    "# !pip install lida[infographics] # for infographics support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lida import Manager, TextGenerationConfig , llm  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize Data, Generate Goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "### Goal 0\n",
       "---\n",
       "**Question:** What is the relationship between engine size and horsepower?\n",
       "\n",
       "**Visualization:** `scatter plot of Engine_Size__l_ vs Horsepower_HP_`\n",
       "\n",
       "**Rationale:** This visualization will help us understand if there is a correlation between engine size and horsepower. By plotting the two variables against each other, we can see if there is a positive or negative correlation, and how strong it is. This information will be useful for understanding the performance of different cars and for making decisions about engine size and horsepower in future car models.\n"
      ],
      "text/plain": [
       "Goal(question='What is the relationship between engine size and horsepower?', visualization='scatter plot of Engine_Size__l_ vs Horsepower_HP_', rationale='This visualization will help us understand if there is a correlation between engine size and horsepower. By plotting the two variables against each other, we can see if there is a positive or negative correlation, and how strong it is. This information will be useful for understanding the performance of different cars and for making decisions about engine size and horsepower in future car models.', index=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "### Goal 1\n",
       "---\n",
       "**Question:** What is the distribution of retail prices across different car types?\n",
       "\n",
       "**Visualization:** `box plot of Retail_Price by Type`\n",
       "\n",
       "**Rationale:** This visualization will help us understand the range and distribution of retail prices for different car types. By using a box plot, we can see the median, quartiles, and outliers for each car type, allowing us to compare the distribution of prices across different types. This information will be useful for understanding the market for different car types and for making pricing decisions for future car models.\n"
      ],
      "text/plain": [
       "Goal(question='What is the distribution of retail prices across different car types?', visualization='box plot of Retail_Price by Type', rationale='This visualization will help us understand the range and distribution of retail prices for different car types. By using a box plot, we can see the median, quartiles, and outliers for each car type, allowing us to compare the distribution of prices across different types. This information will be useful for understanding the market for different car types and for making pricing decisions for future car models.', index=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lida = Manager(text_gen = llm(\"openai\", api_key=None)) # !! api key\n",
    "textgen_config = TextGenerationConfig(n=1, temperature=0.5, model=\"gpt-3.5-turbo-0301\", use_cache=True)\n",
    "\n",
    "summary = lida.summarize(\"https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv\", summary_method=\"default\", textgen_config=textgen_config)  \n",
    "goals = lida.goals(summary, n=2, textgen_config=textgen_config)\n",
    "\n",
    "for goal in goals:\n",
    "    display(goal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "### Goal 0\n",
       "---\n",
       "**Question:** What is the relationship between Retail Price and City Miles Per Gallon for cars?\n",
       "\n",
       "**Visualization:** `scatter plot of Retail Price vs City Miles Per Gallon`\n",
       "\n",
       "**Rationale:** This visualization will help the mechanic identify which cars are both cheap and have good gas mileage. By plotting the Retail Price against the City Miles Per Gallon, we can see if there is a trend in the data that shows which cars offer the best value for money.\n"
      ],
      "text/plain": [
       "Goal(question='What is the relationship between Retail Price and City Miles Per Gallon for cars?', visualization='scatter plot of Retail Price vs City Miles Per Gallon', rationale='This visualization will help the mechanic identify which cars are both cheap and have good gas mileage. By plotting the Retail Price against the City Miles Per Gallon, we can see if there is a trend in the data that shows which cars offer the best value for money.', index=0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\n",
       "### Goal 1\n",
       "---\n",
       "**Question:** What is the distribution of Horsepower for cars with good gas mileage?\n",
       "\n",
       "**Visualization:** `histogram of Horsepower_HP_ for cars with City Miles Per Gallon greater than 30`\n",
       "\n",
       "**Rationale:** This visualization will help the mechanic identify which cars have good gas mileage but still have enough horsepower to meet their needs. By filtering the data to only include cars with City Miles Per Gallon greater than 30, we can see the distribution of Horsepower_HP_ for these cars and determine if there are any outliers or trends in the data.\n"
      ],
      "text/plain": [
       "Goal(question='What is the distribution of Horsepower for cars with good gas mileage?', visualization='histogram of Horsepower_HP_ for cars with City Miles Per Gallon greater than 30', rationale='This visualization will help the mechanic identify which cars have good gas mileage but still have enough horsepower to meet their needs. By filtering the data to only include cars with City Miles Per Gallon greater than 30, we can see the distribution of Horsepower_HP_ for these cars and determine if there are any outliers or trends in the data.', index=1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# goals can also be based on a persona \n",
    "persona = \"a mechanic who wants to buy a car that is cheap but has good gas mileage\"\n",
    "personal_goals = lida.goals(summary, n=2, persona=persona, textgen_config=textgen_config)\n",
    "for goal in personal_goals:\n",
    "    display(goal)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "import seaborn as sns\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
       "# solution plan\n",
       "# i.  ..\n",
       "def plot(data: pd.DataFrame):\n",
       "    sns.scatterplot(data=data, x='Engine_Size__l_', y='Horsepower_HP_')\n",
       "    plt.title('What is the relationship between engine size and horsepower?', wrap=True)\n",
       "    return plt;\n",
       "\n",
       "chart = plot(data)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 0\n",
    "library = \"seaborn\"\n",
    "textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True)\n",
    "charts = lida.visualize(summary=summary, goal=goals[i], textgen_config=textgen_config, library=library)  \n",
    "charts[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate visualization via a \"user query\"   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "import seaborn as sns\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
       "def plot(data: pd.DataFrame):\n",
       "    # solution plan\n",
       "    # i. group data by Type and calculate the mean of Retail_Price\n",
       "    grouped_data = data.groupby('Type')['Retail_Price'].mean().reset_index()\n",
       "    \n",
       "    # ii. plot the grouped data using a barplot\n",
       "    sns.barplot(x='Type', y='Retail_Price', data=grouped_data)\n",
       "    \n",
       "    # iii. add a horizontal line for the mean Retail_Price\n",
       "    mean_price = data['Retail_Price'].mean()\n",
       "    plt.axhline(mean_price, color='red', linestyle='--', label=f'Mean Price: {mean_price:.2f}')\n",
       "    \n",
       "    # iv. add a legend\n",
       "    plt.legend()\n",
       "    \n",
       "    # v. set x-axis label rotation to 45 degrees for legibility\n",
       "    plt.xticks(rotation=45)\n",
       "    \n",
       "    # vi. set y-axis label\n",
       "    plt.ylabel('Average Retail Price')\n",
       "    \n",
       "    # vii. set plot title\n",
       "    plt.title('What is the average price of cars by type?', wrap=True)\n",
       "    \n",
       "    return plt;\n",
       "\n",
       "chart = plot(data)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_query = \"What is the average price of cars by type?\"\n",
    "textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True)\n",
    "charts = lida.visualize(summary=summary, goal=user_query, textgen_config=textgen_config)  \n",
    "charts[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VizOps\n",
    "\n",
    "Given that LIDA represents visualizations as code,\n",
    "the VISGENERATOR also implements submodules\n",
    "to perform operations on this representation. \n",
    "\n",
    "This includes \n",
    "- **Natural language based visualization refinement**: Provides a conversational api to iteratively\n",
    "4Execution in a sandbox environment is recommended.\n",
    "refine generated code (e.g., translate chart t hindi\n",
    ". . . zoom in by 50% etc) which can then be executed to generate new visualizations.\n",
    "- **Visualization explanations and accessibility**:\n",
    "Generates natural language explanations (valuable\n",
    "for debugging and sensemaking) as well as accessibility descriptions (valuable for supporting users\n",
    "with visual impairments).\n",
    "\n",
    "- **Visualization code self-evaluation and repair**:\n",
    "Applies an LLM to self-evaluate generated code on\n",
    "multiple dimensions (see section 4.1.2).\n",
    "\n",
    "- **Visualization recommendation**: Given some context (goals, or an existing visualization), recommend additional visualizations to the user (e.g., for\n",
    "comparison, or to provide additional perspectives).\n",
    "\n",
    "\n",
    "\n",
    "## Natural language based visualization refinement \n",
    "\n",
    "Given some code, modify it based on natural language instructions. This yields a new code snippet that can be executed to generate a new visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "import seaborn as sns\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "import matplotlib as mpl\n",
       "\n",
       "# additional imports\n",
       "mpl.rcParams['figure.figsize'] = [6, 6] # set chart height and width equal\n",
       "mpl.rcParams['font.family'] = 'Arial' # set font family\n",
       "mpl.rcParams['text.color'] = 'black' # set text color\n",
       "mpl.rcParams['axes.labelcolor'] = 'black' # set label color\n",
       "mpl.rcParams['xtick.color'] = 'black' # set xtick color\n",
       "mpl.rcParams['ytick.color'] = 'black' # set ytick color\n",
       "mpl.rcParams['axes.titlesize'] = 14 # set title font size\n",
       "mpl.rcParams['axes.titleweight'] = 'bold' # set title font weight\n",
       "mpl.rcParams['axes.titlepad'] = 20 # set title padding\n",
       "\n",
       "def plot(data: pd.DataFrame):\n",
       "    # solution plan\n",
       "    # i. group data by Type and calculate the mean of Retail_Price\n",
       "    grouped_data = data.groupby('Type')['Retail_Price'].mean().reset_index()\n",
       "    \n",
       "    # ii. plot the grouped data using a barplot\n",
       "    sns.barplot(x='Type', y='Retail_Price', data=grouped_data, color='red') # change chart color to red\n",
       "    \n",
       "    # iii. add a horizontal line for the mean Retail_Price\n",
       "    mean_price = data['Retail_Price'].mean()\n",
       "    plt.axhline(mean_price, color='black', linestyle='--', label=f'Precio Promedio: {mean_price:.2f}') # translate chart to spanish\n",
       "    \n",
       "    # iv. add a legend\n",
       "    plt.legend()\n",
       "    \n",
       "    # v. set x-axis label rotation to 45 degrees for legibility\n",
       "    plt.xticks(rotation=45)\n",
       "    \n",
       "    # vi. set y-axis label\n",
       "    plt.ylabel('Precio Promedio de Venta')\n",
       "    \n",
       "    # vii. set plot title\n",
       "    plt.title('¿Cuál es el precio promedio de los carros por tipo?', wrap=True, fontweight='bold') # translate chart to spanish\n",
       "    \n",
       "    return plt;\n",
       "\n",
       "chart = plot(data)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "code = charts[0].code\n",
    "textgen_config = TextGenerationConfig(n=1, temperature=0, use_cache=True)\n",
    "instructions = [\"make the chart height and width equal\", \"change the color of the chart to red\", \"translate the chart to spanish\"]\n",
    "edited_charts = lida.edit(code=code,  summary=summary, instructions=instructions, library=library, textgen_config=textgen_config)\n",
    "edited_charts[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization explanations and accessibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accessibility  **  The chart is a bar plot created using the seaborn library. The goal of the chart is to show the average retail price of cars by type. The x-axis represents the different types of cars, while the y-axis represents the average retail price. The chart uses a blue color scheme for the bars and a red dashed line to represent the mean retail price. The chart is easy to read and understand, with clear labels and a title that summarizes the main insight.\n",
      "transformation  **  This section of the code groups the data by car type and calculates the mean retail price for each group. The resulting data is stored in a new dataframe called grouped_data. The groupby() method is used to group the data by the 'Type' column, and the mean() method is used to calculate the average retail price for each group. The reset_index() method is used to reset the index of the resulting dataframe.\n",
      "visualization  **  This section of the code creates the visualization of the data. The seaborn barplot() method is used to create a bar chart of the grouped data. The x-axis is set to the 'Type' column, and the y-axis is set to the 'Retail_Price' column of the grouped_data dataframe. The plt.axhline() method is used to add a horizontal line to the chart representing the mean retail price of all cars. The color of the line is set to red, and the linestyle is set to dashed. The label parameter is used to add a label to the line indicating the mean price. The plt.legend() method is used to add a legend to the chart. The plt.xticks() method is used to rotate the x-axis labels by 45 degrees for better legibility. The plt.ylabel() method is used to set the y-axis label to 'Average Retail Price'. The plt.title() method is used to set the chart title to 'What is the average price of cars by type?'. The wrap parameter is set to True to wrap the title text if it exceeds the width of the chart.\n"
     ]
    }
   ],
   "source": [
    "explanations = lida.explain(code=code, library=library, textgen_config=textgen_config) \n",
    "for row in explanations[0]:\n",
    "    print(row[\"section\"],\" ** \", row[\"explanation\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization code self-evaluation and repair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bugs Score 10 / 10\n",
      "\t The code is free of bugs, syntax errors, and typos. It should compile without issues.\n",
      "\t**********************************\n",
      "transformation Score 8 / 10\n",
      "\t The data is grouped by Type and the mean of Retail_Price is calculated. However, this transformation is not appropriate for the specified goal of exploring the relationship between engine size and hor\n",
      "\t**********************************\n",
      "compliance Score 6 / 10\n",
      "\t The code does not fully meet the specified goal of exploring the relationship between engine size and horsepower. The plot shows the average retail price of cars by type instead.\n",
      "\t**********************************\n",
      "type Score 3 / 10\n",
      "\t The barplot is not an appropriate visualization type for exploring the relationship between engine size and horsepower. A scatterplot or line plot would be more appropriate.\n",
      "\t**********************************\n",
      "encoding Score 8 / 10\n",
      "\t The data is encoded appropriately with Type on the x-axis and Retail_Price on the y-axis. However, this encoding is not appropriate for the specified goal of exploring the relationship between engine \n",
      "\t**********************************\n",
      "aesthetics Score 7 / 10\n",
      "\t The aesthetics of the visualization are appropriate for a barplot, with clear labels, a legend, and a title. However, the title does not reflect the specified goal of exploring the relationship betwee\n",
      "\t**********************************\n"
     ]
    }
   ],
   "source": [
    "evaluations = lida.evaluate(code=code,  goal=goals[i], textgen_config=textgen_config, library=library)[0] \n",
    "for eval in evaluations:\n",
    "    print(eval[\"dimension\"], \"Score\" ,eval[\"score\"], \"/ 10\")\n",
    "    print(\"\\t\", eval[\"rationale\"][:200])\n",
    "    print(\"\\t**********************************\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "textgen_config = TextGenerationConfig(n=2, temperature=0.2, use_cache=True)\n",
    "recommended_charts =  lida.recommend(code=code, summary=summary, n=2,  textgen_config=textgen_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended 1 charts\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "import seaborn as sns\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
       "def plot(data: pd.DataFrame):\n",
       "    # solution plan\n",
       "    # i. group data by Type and calculate the mean of Retail_Price\n",
       "    grouped_data = data.groupby('Type')['Retail_Price'].mean().reset_index()\n",
       "    \n",
       "    # ii. plot the grouped data using a barplot\n",
       "    sns.barplot(x='Type', y='Retail_Price', data=grouped_data)\n",
       "    \n",
       "    # iii. add a horizontal line for the mean Retail_Price\n",
       "    mean_price = data['Retail_Price'].mean()\n",
       "    plt.axhline(mean_price, color='red', linestyle='--', label=f'Mean Price: {mean_price:.2f}')\n",
       "    \n",
       "    # iv. add a legend\n",
       "    plt.legend()\n",
       "    \n",
       "    # v. set x-axis label rotation to 45 degrees for legibility\n",
       "    plt.xticks(rotation=45)\n",
       "    \n",
       "    # vi. set y-axis label\n",
       "    plt.ylabel('Average Retail Price')\n",
       "    \n",
       "    # vii. set plot title\n",
       "    plt.title('What is the average price of cars by type?', wrap=True)\n",
       "    \n",
       "    return plt;\n",
       "\n",
       "chart = plot(data)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Recommended {len(recommended_charts)} charts\")\n",
    "for chart in recommended_charts:\n",
    "    display(chart) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infographics (Beta)\n",
    "\n",
    "- Explores using LIDA to generate infographics from an existing visualization \n",
    "- Uses the `peacasso` package, and loads open source stable diffusion models \n",
    "- You will need to run `pip install lida[infographics]` to install the required dependencies.\n",
    "- Currently work in progress (work being done to post process infographics with chart axis and title overlays from the original visualization, add presets for different infographic styles, and add more stable diffusion models)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install lida[infographics] \n",
    "# ensure you have a GPU runtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "infographics = lida.infographics(visualization = edited_charts[0].raster, n=1, style_prompt=\"pastel art, green pearly rain drops, highly detailed, no blur, white background\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lida.utils import plot_raster\n",
    "plot_raster([edited_charts[0].raster, infographics[\"images\"][0]]) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "189101fc34b85ec7417252a331b6b3ef556b71030ac1f6fe00bfbe1409305460"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
